I am working on MMM, I have a small doubt on how to optimize decay rate lambda in adstock using Grid search? Please share a sample code.
I am confused about this, How can we define a model for it.
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I try to find the optimum of a data-driven function represented as a Tensorflow model.
Means I trained a model to approximate a function and now want to find the optimum of this approximated function using a algorithm and software package/python library like ipopt, ipyopt, casadi, .... Or is there a possibility to do this directly in Tensorflow. I also have to define constraints, so I can't just use simple autodiff to do gradient decent and optimize my input.
Is there any idea how to realize this in an efficient way?
Maybe this image visualizes my problem to better understand what I'm looking for.
I'm trying to train model using pycaret and noticed many times that Model performance reduces after Hyperparameter tuning.
I have attached an image showing how i'm trying to tuning.
Can any one suggest what i'm doing wrong?
Thanks in advance!
It also happened to me.After little search ,from documentation;
"In order to tune hyperparameters, the tune_model() function is used. This function automatically tunes the hyperparameters of a model on a pre-defined search space and scores it using stratified cross-validation."
So what i did, I used not only best model also 2nd and 3rd too. Incase some overfitting problems :).
I know it's a banality, but i'm really confused on what Speed (ms) and COCO mAP means HERE.
I get the idea, lower speed and higher mAP are better, but can i ask what does those metrics mean?
I have to write a report about a project that uses one of the model listed in the github model of tensorflow, so i would like a technical description of those two if possible. About COCO mAP i found something already, i'm trying to understand it, but nothing related to Speed. What does speed measure?
I'm sorry about the stupid question, but i like to fully understand things
It refers to inference speed, how much time it take for the network to provide an output based on your input.
Is it really necessary to optimize the initial learning rate when using ADAM as optimizer in tensorflow/keras? How can this be done (in tensorflow 2.x)?
It is. Like with any hyperparameter, an optimal learning rate should be search for. It might be the case that your model will not learn if the learning rate is too big or too small even with an optimizer like ADAM which has a nice properties regarding decay etc.
Example of behavior of a model under ADAM optimizer with respect to a learning rate can be seen in this article How to pick the best learning rate for your machine learning project
Looking for right hyperparameters is called hyperparameter tuning. I am not using TF 2.* in my projects so I will give a reference to what TensorFlow itself offers Hyperparameter Tuning with the HParams Dashboard
I am building a logistic regression model in tensorflow to approximate a function.
When I randomly select training and testing data from the complete dataset, I get a good result like so (blue are training points; red are testing points, the black line is the predicted curve):
But when I select the spatially seperate testing data, I get terrible predicted curve like so:
I understand why this is happening. But shouldn't a machine learning model learn these patterns and predict new values?
Similar thing happens with a periodic function too:
Am I missing something trivial here?
P.S. I did google this query for quite some time but was not able to get a good answer.
Thanks in advance.
What you are trying to do here is not related to logistic regression. Logistic regression is a classifier and you are doing regression.
No, machine learning systems aren't smart enough to learn to extrapolate functions like you have here. When you fit the model you are telling it to find an explanation for the training data. It doesn't care what the model does outside the range of training data. If you want it to be able to extrapolate then you need to give it extra information. You could set it up to assume that the input belonged to a sine wave or a quadratic polynomial and have it find the best fitting one. However, with no assumptions about the form of the function you won't be able to extrapolate.